Inkling-Small

Inkling-Small

Thinking Machines Lab
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About

Gemini 3.5 Flash is Google’s latest frontier AI model designed to combine advanced intelligence, high-speed performance, and agentic workflow execution for developers, enterprises, and everyday users. Built as part of the Gemini 3.5 family, the model excels at coding, long-horizon reasoning, multimodal understanding, and complex multi-step automation tasks while delivering significantly faster output speeds than many competing frontier models. Gemini 3.5 Flash powers AI agents capable of planning, executing, and managing workflows such as application development, codebase maintenance, data analysis, and financial document preparation through the Antigravity harness. The model also supports rich multimodal experiences by generating interactive graphics, dynamic web interfaces, animations, and advanced visual content. Gemini 3.5 Flash is integrated across Google products including the Gemini app, Google Search AI Mode, Google Antigravity, Google AI Studio, Android Studio, and more.

About

Inkling-Small is an efficient model that offers performance comparable to Inkling at a quarter of its size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters, trained on NVIDIA GB300 NVL72 systems. It supports native reasoning across text, images, and audio, variable thinking effort, and context windows of up to one million tokens. Users adjust reasoning effort from minimal to extra high to balance performance and compute. Improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning helped Inkling-Small surpass its larger counterpart on reasoning and coding benchmarks. It performs well in coding and tool-use harnesses, exceeds 80% on SWE-bench Verified, and combines strong reasoning with efficient output. Its encoder-free multimodal architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Developers, enterprises, AI engineers, business teams, and organizations seeking high-performance multimodal AI models for coding, automation, agentic workflows, and intelligent application development

Audience

Developers, AI agent builders, software engineering teams, research teams, enterprise AI teams, multimodal application developers, coding assistant builders, tool-use workflow teams, and organizations that need efficient reasoning, long-context processing, text-image-audio understanding, adjustable thinking effort, coding performance, and scalable Mixture-of-Experts inference

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

$1.50 per 1M tokens (input)
Input: $1.50 per 1 million tokens
Output: $9.00 per 1 million tokens
Free Version
Free Trial

Pricing

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Pros & Cons from Real Users

Pros

  • Gemini 3.5 Flash is seriously impressive when it comes to speed. What stands out most is that it delivers high-end AI performance without feeling slow or heavy. Responses are fast, coding help is excellent, and it handles complex multi-step tasks far better than previous Gemini models. For someone who uses AI constantly throughout the workday, that balance of speed and intelligence makes a huge difference. I’ve also been impressed by how capable it is with agentic workflows. Whether it’s organizing research, generating documents, helping with coding, or handling long prompts with lots of context, 3.5 Flash feels much more reliable and focused. It’s especially useful inside the Gemini app and Workspace tools because it can actually help complete tasks instead of just answering questions. The multimodal improvements are another huge plus. The model handles text, graphics, UI generation, and interactive content much better than before. It feels more polished and more practical for real-world work, especially for developers, analysts, and teams handling large projects.

Cons

  • Even though the model is incredibly fast, there are still occasional moments where outputs need refinement, especially for highly technical or nuanced requests. I still review important drafts, code, or generated workflows before fully relying on them.

Pros & Cons from Real Users

Pros

  • Inkling-Small is really interesting from a developer’s point of view because it hits a sweet spot between serious model capability and practical deployability. A 276B-parameter model with only 12B active parameters per token is exactly the kind of architecture that makes sense if you care about cost, speed, and scaling real AI workflows. I also like that it is open weights under Apache 2.0. That makes it way more appealing for developers who want to fine-tune, inspect, customize, or build on top of the model without being completely locked into a closed API. The multimodal support is a big plus too. Being able to work with text, images, and audio inputs gives Inkling-Small a lot of room for developer tools, coding agents, support bots, document workflows, and internal automation.

Cons

  • The main downside is that “small” here is still not tiny. Even with only 12B active parameters, this is still a large open model that will require real infrastructure if you want to host it yourself. I would also want to test it deeply before making it the backbone of a production coding agent. The model card and early coverage look promising, but real developer workflows expose problems that benchmarks do not always catch: messy repos, flaky tests, weird dependencies, tool failures, and long multi-step tasks.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Google
Founded: 1998
United States
gemini.google.com

Company Information

Thinking Machines Lab
Founded: 2025
United States
thinkingmachines.ai/news/inkling-small/

Alternatives

Claude Fable 5

Claude Fable 5

Anthropic

Alternatives

Claude Mythos 5

Claude Mythos 5

Anthropic
Claude Opus 5

Claude Opus 5

Anthropic
Inkling

Inkling

Thinking Machines Lab
Gemini 4

Gemini 4

Google

Categories

Categories

Integrations

Android Studio
C
C#
C++
Devin Desktop
Gemini 3.5 Flash Cyber
Google
Google AI Mode
Google AI Studio
Google Antigravity
JavaScript
Kotlin
Lua
Model Context Protocol (MCP)
PHP
PowerShell
Replit
Ruby
Solidity
TypeScript

Integrations

Android Studio
C
C#
C++
Devin Desktop
Gemini 3.5 Flash Cyber
Google
Google AI Mode
Google AI Studio
Google Antigravity
JavaScript
Kotlin
Lua
Model Context Protocol (MCP)
PHP
PowerShell
Replit
Ruby
Solidity
TypeScript
Claim Gemini 3.5 Flash and update features and information
Claim Gemini 3.5 Flash and update features and information
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